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Future concepts and key technologies for reusable launch vehicles are currently investigated by the DLR project AKIRA, focusing on vertical takeoff and horizontal landing (VTHL), as well as horizontal takeoff and horizontal landing (HTHL) concepts. Dedicated developments of multidisciplinary frameworks for launch vehicle modeling and preliminary design optimization have been presented in the relevant literature. These activities are often performed by several independent and discipline-specific tools; such an approach can only account for limited interactions of the involved disciplines with the overall system dynamics. Therefore, the objective of this paper is to focus on a multidisciplinary launch vehicle dynamics modeling, guidance, and control framework to support reusable launch vehicle design activities at DLR while taking into account the highly interconnected disciplines involved and changing environmental conditions. The modeling framework is based on the object-oriented, multidisciplinary, and equation-based modeling language MODELICA. Dedicated 3-DOF and 6-DOF model implementations, covering the kinematics and dynamics formulation, environmental effects, aerodynamics, and propulsion models are presented. Within this framework, a method to obtain a direct connection between 3-DOF and 6-DOF models is shown. This is done by considering results from the trajectory optimization package ' trajOpt ' in combination with nonlinear 6-DOF inverse models obtained automatically by MODELICA. Angular rates and resulting moments can be retrieved by this intermediate 6-DOF modeling approach for subsequent controllability studies. We discuss some of these benefits in terms of nonlinear flight control simulations for an HTHL reusable launch vehicle concept. • Multidisciplinary simulation tool for preliminary design studies of launch vehicles. • Framework based on the multi-physics and acausal modeling language MODELICA. • Dynamics modeling, guidance, control, and simulation activities are supported. • Nonlinear inverse models allow fast transition between 3-DOF and 6-DOF models. • Nonlinear flight control simulations are performed for the RLV concept AURORA. [ABSTRACT FROM AUTHOR]

Chassis control systems play a significant role in achieving the desired vehicle performance and stability during various severe maneuvers. A probabilistic estimation approach by hybridization of optimal robust control and a damped least-square backpropagation based neural networks (NN) is proposed to design a control system for dealing with unknown nonlinear dynamics of a passenger car. To this end, a four-wheel active steering (4WAS) model is employed and a multilayer perceptron (ML) feed-forward backpropagation neural network (FFBPNN) model is developed as an approximator. The optimal robust control is employed to regulate the yaw rate and side-slip angle of the vehicle to follow the desired vehicle response. The developed FFBPNN model is trained to distinguish the nonlinear dynamics of the vehicle and the corresponding optimal feedback gain during a wide range of operating conditions via the state variables. The robustness of the controller is evaluated using Lyapunov stability method. The performance of the proposed controller is analyzed considering the open-loop and closed-loop responses of the nonlinear vehicle model and a sliding mode controller to track the desired yaw rate and side-slip angle responses. The results obtained during severe maneuvers suggest that the proposed control method can substantially enhance the handling and stability performances of the vehicle. [ABSTRACT FROM AUTHOR]

In this work, the coupled control of the lateral and the longitudinal dynamics of an autonomous vehicle is addressed. As a first step, a multi-body modeling technique is used to develop a four wheeled vehicle planar model. This technique considers the vehicle as a robot consisting of articulated bodies. The geometric description of the vehicle system is derived using the modified Denavit–Hartenberg parameterization and then the dynamic model of the vehicle is computed by applying a recursive method used in robotics, namely Euler–Lagrange based Algorithm. The validation of the developed vehicle model was then conducted using an automotive simulator, the Scaner-Studio simulator. The developed vehicle model is then used to derive coupled control laws for the lateral and the longitudinal vehicle dynamics. Two coupled controllers are proposed: In the first controller, the control is realized using Lyapunov control techniques while in the second one an Immersion and Invariance with sliding mode approach is used. Both of the controllers aim to ensure a robust tracking of the reference trajectory and the desired speed while taking into account the strong coupling between the lateral and the longitudinal vehicle dynamics. In fact, the coupled controller is a key step for the vehicle safety handling, especially in coupled maneuvers such as lane-change maneuvers, obstacle avoidance maneuvers and combined maneuvers in critical driving situations. The developed controllers were validated in simulation under Matlab/Simulink using experimental data. Subsequently, an experimental validation of the proposed controllers was conducted using a robotized vehicle (Renault-ZOE) present in the Heudiasyc laboratory within the Equipex Robotex project. [ABSTRACT FROM AUTHOR]

Highlights • A hardware-in-the-loop (HIL) testbed with an actual engine and a transient dynamometer. • The HIL testbed emulates a virtual target vehicle with simulated vehicle dynamics. • Performance of the virtual target vehicle matches an actual vehicle with 1% error. • A living lab enables HIL testbed to interact with on-road vehicles in real-time. • An effective and economical way to evaluate connected and autonomous vehicles. Abstract Connected and autonomous vehicle (CAV) applications focusing on energy optimization have attracted a lot of attention recently. However, it is challenging to evaluate various optimizations and controls in real-world traffic scenarios due to safety and technical concerns. In light of this, our previous work has developed a hardware-in-the-loop (HIL) testbed with a laboratory powertrain research platform to evaluate CAV applications. An actual engine is loaded by a hydrostatic dynamometer whose loading torque is controlled in real-time by the simulated vehicle dynamics. The HIL testbed mimics the performance of a target vehicle and the dynamometer generates the same load as the target vehicle. In this work, the HIL testbed is further enhanced to match the performance of actual testing vehicles at the Federal Highway Administration (FHWA) and a living lab is developed to incorporate real traffic information. The same engine as the actual testing vehicles at the FHWA was installed and the vehicle models were calibrated using testing data from actual vehicles. The same roadway conditions (speed limit, the degree of road slope, etc.) were input into the testbed and the dynamometer generates the same load as an actual vehicle's engine is getting. The HIL testbed emulates the performance of an actual vehicle and both fuel consumption and emissions are measured by precise instruments in the lab. In eight testing scenarios, the results have shown that the performance of the HIL testbed matches very well with the actual testing vehicles with about 1% error. In addition, the living lab has enabled the HIL testbed to interact with real traffic and extended the testing capabilities of the HIL testbed. Two new testing capabilities have been demonstrated through two CAV applications. This is an exciting result since the HIL testbed could provide an effective and economical way for the testing of fuel consumption and emissions on various roadway conditions for CAVs and other types of vehicles. [ABSTRACT FROM AUTHOR]

Highlights • We evaluated fuel consumption impacts of autonomous vehicles in United States. • The proposed data-rich analytical framework considers dynamics in fleet turnover and fuel efficiency. • We utilized a network activity database contains detailed information of national road links. • Impacts of automation on fuel consumption are quite wide-ranging, −45% to +30%. • Automation on urban road has larger potential on fuel savings than highway automation. Abstract Autonomous vehicles are drawing significant attention from governments, manufacturers and consumers. Experts predict them to be the primary means of transportation by the middle of this century. Recent literature shows that vehicle automation has the potential to alter traffic patterns, vehicle ownership, and land use, which may affect fuel consumption from the transportation sector. In this paper, we developed a data-rich analytical framework to quantify system-wide fuel impacts of automation in the United States by integrating (1) a dynamic vehicle sales, stock, and usage model, (2) an historical transportation network-level vehicle miles traveled (VMT)/vehicle activity database, and (3) estimates of automation's impacts on fuel efficiency and travel demand. The vehicle model considers dynamics in vehicle fleet turnover and fuel efficiency improvements of conventional and advanced vehicle fleet. The network activity database contains VMT, free-flow speeds, and historical speeds of road links that can help us accurately identify fuel-savings opportunities of automation. Based on the model setup and assumptions, we found that the impacts of automation on fuel consumption are quite wide-ranging—with the potential to reduce fuel consumption by 45% in our "Optimistic" case or increase it by 30% in our "Pessimistic" case. Second, implementing automation on urban roads could potentially result in larger fuel savings compared with highway automation because of the driving features of urban roads. Through scenario analysis, we showed that the proposed framework can be used for refined assessments as better data on vehicle-level fuel efficiency and travel demand impacts of automation become available. [ABSTRACT FROM AUTHOR]

• A unified reconfigurable vehicle dynamics model is developed in this study. • A novel approach in vehicle modeling is presented to develop the proposed model. • The model can be configured for different four- and three-wheeled vehicles. • Wheels and actuators can readily be added or removed using the defined matrices. • Included actuators are active steering, differential braking and torque vectoring. This study presents a novel approach in vehicle modeling to provide a unified reconfigurable vehicle dynamics model. The introduced vehicle model is applicable for analysis, design, and control of a wide variety of vehicles and can be reconfigured for four- and three-wheeled vehicles. Active steering, differential braking, torque vectoring, and active camber are included in the model as the actuators. The proposed set of model equations is reconfigurable and can be adjusted for various vehicles without need for new derivation. Two matrices are defined called the corner reconfiguration matrix and actuator reconfiguration matrix that are responsible for wheel and actuator configurations of the vehicle, respectively. The model can be reconfigured by setting the diagonal elements of these two matrices. The proposed reconfigurable model is compared with different high-fidelity vehicle models in CarSim for validation. Simulation results show that four-wheeled and three-wheeled vehicle models can all be drawn from the single unified reconfigurable model. In addition, the introduced model properly matches the high-fidelity CarSim models for four-wheeled and three-wheeled vehicles. [ABSTRACT FROM AUTHOR]

• A semi-recursive vehicle model is validated in modeling and accuracy. • Constant and adaptive time-step algorithms for real-time simulation are presented. • An accurate real-time simulation of a vehicle is completed in an affordable laptop. • A benchmark problem for the multibody dynamics community is put forward. Semi-recursive formulations and their various versions have made it possible to describe complex nonlinear systems such as vehicles precisely while still solving the relevant equations of motion in real time. An optimal combination of an efficient multibody formulation and a fast numerical time integration scheme are needed to accurately simulate complex systems in real time. This paper introduces a double-step semi-recursive multibody formulation and analyzes its performance with high-order numerical time-integration algorithms for real-time simulation. The Runge-Kutta, Gill, Runge-Kutta-Fehlberg, Adams-Bashforth-Moulton, and adaptive time step Runge-Kutta numerical methods are explained and compared. Results are verified against a commercial multibody software solution. A 15-degree-of-freedom sedan vehicle model serves as a benchmark to verify the theoretical results. The results highlight the differences between the numerical algorithms and suggest appropriate approaches for a nonlinear vehicle dynamics model, particularly for cases where simulation times are long. [ABSTRACT FROM AUTHOR]

This work compares methods to compute confidence bands in a validation task of a vehicle single-track model. The confidence bands are computed from time series by naïve method, Gaussian process regression and heteroscedastic and non-stationary Gaussian process regression. The simulation model considers the epistemic uncertainty of the vehicle mass parameter by Latin hypercube sampling. The validation procedure compares all stochastically simulated time series of the vehicle yaw rate with the confidence band of the reference data. The model is marked as valid if the yaw rate for each time step is within the confidence band of the reference data. The data was challenging due to noise and time-varying variance and smoothness. Due to required data pre-processing and the high sensitivity to noise in the reference data, the naïve method has generated unusable confidence bands and cannot be recommended for similar validation tasks. Gaussian process regression solved the problem of noise sensitivity, but was not able to model the time-varying length scale of the reference data. Therefore, heteroscedastic and non-stationary Gaussian process regression is proposed to calculate accurate confidence bands of time-varying and noisy reference data for the validation of dynamic models by a confidence band approach. [ABSTRACT FROM AUTHOR]

• Propose a cooperative platoon vehicles dynamics model to analyze cyberattack effects. • Apply a bi-layer structure to capture the spread of malicious messages. • Incorporate a dynamic communication topology to factor cyberattack effects. • Conduct simulation to illustrate the cyberattack effects on CAV platoon dynamics. Vehicular platooning requires an open-access environment of vehicle communications to enable cooperating connected automated vehicles (CAVs) to share their travel information. However, the open-access environment makes the cooperating platoon CAVs vulnerable to cyberattacks, causing concerns on traffic safety and mobility. So far, due to the lack of analytical models integrating malicious effects of manipulated information into CAV dynamics, we have limited knowledge on CAV platoon performance under cyberattack. It necessities a modeling framework to support a comprehensive analysis of the CAV platoon performance in a capricious travel environment. This study seeks to bridge this critical gap from the following three aspects. First, we review potential safety-related cyberattacks faced by CAVs, related to manipulation of vehicle position, speed, and acceleration. Based on the review, these cyberattacks are categorized into three types: bogus messages, replay/delay, and collusion attack. Second, we develop a generalized vehicle dynamics model that accounts for the cyberattack effects on CAV dynamics. The proposed model, labeled cooperative intelligent driver model (CIDM), integrates the dynamic communication topological structure to enable the analysis of effects of manipulated information on CAV dynamics. The dynamic communication structure is a time-varying function which is determined by the communication range and the distance between two vehicles. Upon the proposed CIDM, the third aspect focuses on simulation analysis of CAV platoon safety and efficiency under cyberattacks, which demonstrate the cyberattack effects at the platoon level. This study contributes to the fundamental understanding of CAV platoon dynamics under cyberattacks and lays a foundation to enhance the cybersecurity of CAV systems. [ABSTRACT FROM AUTHOR]

In the paper, a robust controller using the back-stepping approach and the time delay estimation(TDE) is proposed for the trajectory tracking of torpedo-shape autonomous underwater vehicles(AUVs). The AUV has several difficult issues to design accurate tracking control: unsatisfaction of matching condition and highly nonlinear dynamics including external disturbances. To overcome the matching condition issues, the proposed controller is designed based on the back-stepping approach, which is a well-known method handling unmatched disturbances effectively and having well-established design procedure. To estimate nonlinear vehicle dynamics including sea currents and external disturbances, the TDE, an efficient estimation method using previous system information, is adopted. Owing simplicity of the TDE, the proposed controller does not need whole vehicle dynamics while requiring only the determination of control gains. The stability analysis of the proposed controller is studied, conducting the guidelines for selection of control gains. Through the simulation results using REMUS model, it is demonstrated that the proposed controller has accurate and robust tracking performance even when a vehicle moves under sea currents and external disturbing forces. • A robust tracking controller is proposed for torpedo-like underactuted AUVs. • The controller is robust against sea currents and external disturbing forces. • Not requring whole vehicle model, the controller is easy to be desiged. • The design guidelines based on the stability analysis are addressed. • The robustness of the controller is demonstrated through simulation with REMUS model. [ABSTRACT FROM AUTHOR]

The wheel-wear characteristics of the motor and unpowered car of a high-speed train were investigated, for the first time, by a wheel-wear prediction model comprising a wheel-wear sub-model, an unpowered car dynamics sub-model, and a novel motor-car dynamics sub-model. The motor-car dynamics model considers the detailed structural characteristics and working mechanics of the traction transmission system. Assuming the real track parameters and track irregularities, it handles the nonlinear factors such as the traction characteristics, gear backlash, time-varying mesh stiffness, gear friction and wheel–rail contact. This study assessed the wheel-wear characteristics of a high-speed train by suitable performance indices (wheel-wear depth and contact patch energy). Finally, the model was validated by comparing the simulation results with those of field tests. The predicted and measured wheel wear were in good agreement. Both the wheel wear depth and contact energy were higher for the motor car than the unpowered car, because the traction torque on the wheelsets of the motor car increased the longitudinal creepage. During one re-profiling cycle, the transmission stability of the gear transmission system worsened with continuous wheel-wear. Furthermore, the proposed methods can assess the wheel wear and working status of the traction transmission system in the vehicle vibration environment of any rail vehicle. • A novel spatial vehicle dynamics model of high-speed train considering gear transmission system was established. • The wheelwear prediction model of the newly motor and unpowered car are established based on the dynamics model and wheel wear model. • Revealing the wheel wear characteristics between motor and unpowered car, which are supported by experimental tests. • The dynamic performance evolution of gear transmission system in one re-profiling cycle were investigated for the first time. [ABSTRACT FROM AUTHOR]

This article presents an innovative control approach for autonomous racing vehicles. Linear Parameter Varying (LPV) theory is used to model the dynamics of the vehicle and implement an LPV-Model Predictive Controller (LPV-MPC) that can be computed online with reduced computational cost. The optimal time problem is solved by an optimal off-line trajectory planner that calculates the best trajectory under the constraints of the circuit. An identification of the system model based on optimization is also carried out. The planning and control scheme is validated in simulation and experimentally in a real platform where the effectiveness of the proposed LPV-MPC is demonstrated. [ABSTRACT FROM AUTHOR]

• The real-time vehicle simulation is performed via tree-topology-oriented modeling. • The semirecursive vehicle system is modeled using the rod-removal technique. • Canonical system tree topologies are produced using loop-closure techniques. • How the canonical system tree topology affects efficiency is investigated. Multibody dynamics of vehicle systems can be simulated in real time using semi-recursive-based formulations and their various versions, which are normally described in terms of relative coordinates. To efficiently and accurately simulate closed-loop vehicle multibody systems, a semi-recursive formulation must be optimally combined with a numerical integration algorithm. Although a significant number of contributions has been reported in this field, there is still a need to improve accuracy and computational efficiency of real-time or faster-than-real-time simulation. This paper focuses on the loop-closure techniques that can be used when modeling vehicle systems. Consequently, a canonical tree topology is introduced to take advantage of recursive kinematics and dynamics. To this end, real-time vehicle simulations are performed based on a semi-recursive formulation and a tree-topology-oriented modeling method. The applied semi-recursive formulation makes use of double velocity transformation and independent coordinates. The introduced canonical tree topology is generated via joint-cut and rod-removal techniques. The structures of the canonical tree topologies are studied in terms of bodies, joints, constraint equations and branches. How they affect solution accuracy and computational efficiency is investigated in detail. For numerical examples, a 15-degree-of-freedom sedan vehicle model is analyzed and simulated using the semi-recursive formulation and a 4th-order Runge–Kutta algorithm in various system-tree topology cases. The results highlight the efficiency gain of the tree-topology-oriented modeling method. [ABSTRACT FROM AUTHOR]

The paper presents a survey of the existing mathematical models of electric vehicles followed by state of the art approaches on modeling hybridization of the energy sources. The mathematical representation of the electric vehicle (EV) ranging from the simple single Degree of Freedom (DOF) models to complex multi-body dynamic models is discussed in detail. Reduced dynamic models applicable to various control design diligence are discussed which facilitates the selection of the optimal model for design. In addition to vehicle dynamics, the paper consolidates dynamic models of the different components of an electric vehicle including the transmission, brake, battery, wheel and tire dynamics. Comparative analysis of different versions of the models for each component is also presented, focusing on their application in controller design. The paper thus acts as a guide for any EV control design requirement, providing optimal models for particular applications. • The paper, first of its kind, summaries existing EV models facilitating control design. • Several vehicular models presented, varying in complexity from bicycle to multibody model. • Various models of each EV physical component compiled together to ease model selection. • A comparative analysis of existing battery & fuel cell models with model description. • The paper can usher EV control designers with apt models that fit design requirements. [ABSTRACT FROM AUTHOR]